11115 research outputs found
Sort by
Examining the Effect of Contractor Logistics Support on the Reliability of Military Aircraft
Get PDFThis study utilizes survival analysis for examining the effect of Contractor Logistics Support (CLS) on the reliability of military aircraft before and after implementing CLS. The provider of CLS in this study is the original equipment manufacturer that designed and produced the target aircraft of this study, the Embraer A-29 Super Tucano
Qualitative Insights from DoD\u27s Cost Community: Data Visualization Challenges and Recommendations
Get PDFData visualization plays a significant role in decision-making for military leadership. Cost organizations create and utilize data visualizations to reap cost-saving benefits, boost productivity, and expedite task processes. Our research aims to support the development of such tools and provide insights from cost leadership. To do this, we conducted semi-structured interviews with cost professionals to learn how cost professionals manage and view their data, as well as what day-to-day challenges they face while working in the cost community. We used grounded theory for the interview process to detect common trends and attempted to generate new theory from the interview data. Our results will aid academics and practitioners hoping to design useful data analytic and visualization tools
Analyzing the Effects of Atmospheric Turbulence on Polarization-Entangled Photon Pairs Using Quantum State Tomography
Get PDFTo help in building a quantum-based communication link, we experimentally designed a system to simulate atmospheric turbulence and characterize its effects on a polarization-entangled photon-pair source. The simulated turbulence is constructed using two afocal optical systems with a phase plate inserted in each to mimic both weak and strong atmospheric turbulence respectively. After propagation, quantum state tomography (QST) is performed on each pair to reconstruct the density matrix of the pair’s overall polarization state. In characterization of the simulated turbulence, we were able to reach strengths up to a D/r0 of 18.2, which begins to approach the strong turbulent regime. Even in such strengths, the tomography results indicate a high level of resilience, with only marginal change in its polarization angle through the turbulence
Optimizing Deployment Kit, Introducing Acceptance Threshold
Get PDFThis article addresses the optimization of a specialized deployment kit crucial for air force operations, emphasizing the need for rapid and efficient aircraft deployment. Through a comprehensive analysis of historical data, the study aims to streamline the kit\u27s contents without compromising effectiveness. The research suggests a potential reduction of 7.8%, with an impressive 60.5% decrease if a minimal 5% threshold is deemed acceptable. While the primary focus is on air force deployment, the broader implications extend to military entities, such as armies and navies, highlighting the applicability of the findings in enhancing deployment efficiency across various defense sectors
Electric Vehicle Support Equipment Deployment at Military Installations: A Mixed-Integer Linear Programming Approach
Get PDFThis research provides insights into a mixed integer linear programming model that finds the ideal number and type of Electric Vehicle Support Equipment (EVSE) required to meet U.S. military installations’ electric energy demands. Executive Order No. 14057 (2021) requires federal agencies to transition to electric non-tactical vehicles by 2035. This research determines minimum cost solutions to implement the transition incorporating real-world constraints, including the weekly vehicle mileage demand, EVSE cost, charging time, and EVSE capacity. This study contributes to the broader effort of the U.S. military to combat climate change and enhances the understanding of efficient EVSE deployment strategies in large institutions
An Integer Programming Model to Optimize US Army Deployment Cycle and Maximize Unit Availability
Get PDFThe goal of this paper is to determine an optimal cycle length, in months, that minimizes costs and maximizes availability for deploying units in the United States (US) Army. The US Army must be cost efficient while maintaining the flexibility required to adapt to dynamic mission demand. The current practice is to deploy units for a length between the range of 6 to 12 months; however, this varies from unit to unit and the best policy is not clear. We address these issues by forming a mathematical programming model with unique characteristics that distinguish it from others of similar design. These characteristics include meeting monthly demand, handling deployment requirements, minimizing cost, and responding to crises. Current results indicate that shorter deployment lengths have the best expected objective function value. Additionally, we include a supplemental model that reschedules unit deployments in response to a “crisis” event. Tests of the supplemental model indicate a mixed ability respond to such demand surges, but with additional units the model can re-optimize and meet all demands. The US Army can employ these results by applying specific mission demand and unit data to inform decisions relating to efficient deployment scheduling
The Use of Deep Learning and Transfer Learning in Complex Problems
Get PDFDeep neural networks and transfer learning show potential in addressing complex problems such as the Tower of Hanoi and knapsack problems. The primary aim is to examine how the use of deep neural networks and transfer learning can enhance the ability of artificial learning systems to generalize. Transfer learning plays a crucial role in machine learning, particularly in the domain of artificial neural networks, as it helps overcome the challenges associated with limited data, computational efficiency, and generalization. The methodology used in this research involves the creation of data sets for the Tower of Hanoi and knapsack problems. To predict solution sequences and optimize problem solving strategies, a combination of convolutional neural networks (CNNs) and long-short-term memory (LSTM) models is used. The findings demonstrate that the CNN model effectively identifies recursive patterns in the Tower of Hanoi problem. Furthermore, a hybrid CNN-LSTM model shows promise in solving the knapsack problem, although it encounters difficulties in learning capacity constraints. Consequently, a post-processing step is necessary to ensure compliance with these constraints
Federated Analysis of Wearables Data for United States Air Force Mental and Physical Readiness
Get PDFThis thesis investigates the use of machine learning and deep learning models within a federated learning framework to predict physical and mental readiness in military personnel, using wearable technology data. The collaboration with the 711th Human Performance Wing’s STRONG Lab highlights the importance of readiness as emphasized by the National Defense and Security Strategies. The study evaluates various predictive models, incorporating federated learning to ensure data privacy and security in healthcare systems. By analyzing a comprehensive dataset, the research aims to contribute to military readiness enhancement through technological advancements, supporting health and wellness initiatives to bolster the effectiveness of military forces. The findings highlight the potential of machine learning in operational readiness, suggesting future directions for expanding wearable device data integration
Preliminary Analysis of Desirable Cislunar Orbits for Positioning, Navigation and Timing (PNT) at the Lunar South Pole, Surface, and Earth-Moon Corridor
No full textWith the race to the moon starting anew in the 21st century, intensified mission assets are required to perform on the cis-lunar stage, including on the lunar surface and in cis-lunar space. As lunar missions grow in number and increase in complexity, the requirement for persistent position, navigation, and timing (PNT) capabilities become crucial. This paper proposes several lunar PNT architectures with constant coverage of the lunar South Pole, near constant coverage of the whole lunar surface, and coverage of the Earth-Moon corridor. Investigated in this research are orbits propagated in the Circular Restricted Three Body Problem (CR3BP) and Bi-Circular Restricted Four Body Problem (BCR4BP). Orbits investigated include the Halo Family and Distant Retrograde. Several orbits of favor are also discovered through Poincaré Mapping to include a 3:1 Resonant Orbit and an XGEO orbit. Further analysis such as positional dilution of precision, stability, visibility coverage, and PNT system power considerations is performed on these architectures to narrow options to the best fit. For South Pole coverage, an L2 Southern Halo Orbit is chosen for its constant coverage of the lunar south pole. For whole lunar surface coverage, two constellations of Halos and DROs tie for the chosen architecture. Both offer favorable outcomes depending on the mission. For Earth-Moon coverage, a 3:1 Resonant Orbit with a DRO and 2.5 XGEO orbit are considered the best option; however, a conclusion if Earth-Moon coverage is even probable due to its large expanse of space is proposed